The patch is the basic unit to extract local contextual information. In the above experiment, we adopt ConAM at the beginning of each block of ResNet-50 [1]}, and the patch size is set to [16, 16, 8, 4]. However, the network width is gradually increasing, and the spatial size is gradually shrinking, so the deeper the layer, the larger the area where its receptive field radiates to the front layer. Considering that the early feature maps mainly contain low-level semantic features, such as the edges and corners, the extracted average value can reflect the local distribution, but the later feature maps contain high-level semantic features, and the average value extracted lose the meaning of local distribution. Therefore, we design three combinations of patch sizes, i.e., [8, 4, 2, 0], [8, 8, 2, 0] and [8, 8, 4, 0]. 0 means no ConAM is added. In addition, we employ the other function, Sigmoid, to verify the normalization impact on the performance of ConAM.
